Unsupervised Video Object Segmentation On 11

평가 결과

이 벤치마크에서 각 모델의 성능 결과

		Paper Title
FakeFlow	84.7	Improving Unsupervised Video Object Segmentation via Fake Flow Generation
DPA	83.4	Dual Prototype Attention for Unsupervised Video Object Segmentation
TMO++ (MiT-b1)	83.2	Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation
GSANet	83.1	Guided Slot Attention for Unsupervised Video Object Segmentation
TMO++ (RN-101)	81.2	Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation
TMO (MiT-b1)	80.0	Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
TMO (RN-101)	79.9	Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
TransportNet	78.7	Deep Transport Network for Unsupervised Video Object Segmentation
PMN	77.7	Unsupervised Video Object Segmentation via Prototype Memory Network
IMP	77.5	Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier
F2Net	77.5	F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation
AMC-Net	76.5	Learning Motion-Appearance Co-Attention for Zero-Shot Video Object Segmentation
MATNet	76.1	Motion-Attentive Transition for Zero-Shot Video Object Segmentation
COSNet	75.6	See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks
PDB	74.0	Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection

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